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DTSC13-305: Financial Trading Systems September 2020 [Standard - Financial Trading Systems]

General information

This subject provides the opportunity to learn the tools and strategies used by investment and hedge fund managers to invest and trade in a number of financial instruments, including equities, futures, FX and ETFs in both low and high-frequency environments. Using financial data drawn from a variety of sources including Bloomberg, you will learn to model and benchmark these strategies using Python. The overall aim of this applied, research-focused subject is to explore quantitative trading strategies used to capitalise on market anomalies.

Details

Academic unit:Bond Business School
Subject code:DTSC13-305
Subject title:Financial Trading Systems
Subject level:Undergraduate
Semester/Year:September 2020
Credit points:10

Delivery & attendance

Timetable: https://bond.edu.au/timetable
Delivery mode:

Standard

Workload items:
  • Lecture: x12 (Total hours: 24) - Weekly Lecture
  • Computer Lab: x12 (Total hours: 24) - Weekly Computer Laboratory
  • Personal Study Hours: x12 (Total hours: 72) - Study time and reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible.

Resources

Prescribed resources: None
[email protected] & Email:[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.

To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au

Enrolment requirements

Requisites: ?

Nil

Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Possess demonstrable knowledge in basic data science concepts and techniques to the level of a unit such as DTSC12-200 Data Science

Restrictions: ?

Nil

Assurance of learning

Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.

At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.

Program Learning Outcomes (PLOs)

Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:
  1. Apply advanced tools and algorithmic approaches to model risk/reward relationships.
  2. Critically evaluate core issues and trends related to financial trading and investing.
  3. Critically evaluate the use of algorithmic approaches to advanced systems design.

Assessment

Assessment details

TypeTask%Timing*Outcomes assessed
Skills Assignment Case analysis of financial data using Python 10% Week 6 1.
Skills Assignment Case analysis of financial data using Python 30% Week 12 1.
Computer-Aided Examination (Open) Comprehensive final examination 40% Week 13 2, 3.
Computer-Aided Examination (Open) Mid-semester examination 20% Week 8 (Mid-Semester Examination Period) 2, 3.
  • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
  • C = Students must reach a level of competency to successfully complete this assessment.

Assessment criteria

High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.
Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.
Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.
Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment.
Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.

Quality assurance

For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Study information

Submission procedures

Students must check the [email protected] subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.

Policy on plagiarism

University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.

Bond University utilises Originality Reporting software to inform academic integrity.

Feedback on assessment

Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.

Disability support

If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.

Additional subject information

As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Subject curriculum

An overview of quantitative and algorithmic trading and investing and an introduction to the tools and technologies used in the industry.

All successful algorithmic approaches to investing are underpinned by stylised facts. Typically, these take the form of statistical, technical, or fundamental anomalies. What is the basic range of anomalies which lead to successful algorithmic exploitation?

An overview of modern cloud based infrastructure techniques to access data over the web using data pipelines.

Explores the techniques used to find alpha factors – those factors that have predictive relationships with future returns.

Introduces the concept of the Long-Short algorithm, and the frameworks used to build and test these algorithms.

Introduces the detailed analysis and performance attribution requirements used to test complex investment algorithms.

Long-Short equity algorithms are deployed with risk constraints, objectives, and portfolio optimization techniques.

Algorithms focused on harvesting alpha using rotational techniques are explored in detail.

Factor portfolios are constructed using cross-sectional and time-series momentum, and are comprehensively tested.

The techniques used to develop and deploy algorithms to invest in futures markets are explored in detail.

A survey and discussion of open issues and future directions in the field of computational and algorithmic finance.

Approved on: Oct 21, 2020. Edition: 1.3